In many fluid systems, it is important to know the size, concentration and nature of particulates in the fluid. This is true for both undesirable particles in a fluid (i.e. contaminants) and desired particles in a fluid (i.e. additive particles).
In systems that utilize fluid for fuel, lubrication, transfer of power, and/or heat exchange, it is important that minimum levels of cleanliness with respect to solid and liquid particulates be maintained. Solid and liquid particulates in the fluid can damage system components, shorten life, and reduce performance.
An example of a contamination sensitive system is a high pressure common rail fuel injection system for a diesel engine. A common rail fuel injection system operates at very high pressures with clearances between moving parts approaching 1 μm. Excessive wear of injector components caused by abrasives results in loss of control of fuel injection and increased exhaust emissions. Therefore, fine filters are typically used to protect the fuel injection system. However, these filters are subject to plugging by soft contaminants, such as asphaltenes and biological material. In addition, water droplets can cause corrosion and provide an environment for biological growth to occur. The presence of other particulates in the fuel, such as soot or wear metals, is indicative of other issues. It is clear that there are many possible types of contaminants, and that each type causes different problems and requires a different solution.
In the past, to diagnose and solve a typical contamination-related problem, a significant amount of data in terms of engine history, analysis of failed components, fuel analysis and other data, has been used. However, this data is frequently unavailable or incompletely available, short of significant time and effort on the part of service personnel.
In many cases, the most important data needed to correctly diagnose a contamination problem is the size, concentration and nature of the particulate contamination. For example, with respect to fuel for a diesel engine, it is useful to know whether or not the primary contaminants are harmless air bubbles, corrosive water drops, abrasive silica, or bacteria that plug filters. These different contaminants require different responses. A limitation of conventional diagnostic approaches is that the particulate size, concentration and nature must often be inferred by indirect means. The inference can only be tested by continued use and monitoring of the situation and, may require confirmational testing using non-routine analytical methods. This limits the speed and accuracy of diagnosis, and may result in costly delays in implementing solutions.
A technically possible, albeit impractical alternative diagnostic approach, is a series of specific analyses as soon as a problem is detected, in order to obtain accurate size, concentration and nature data. This is impractical for several reasons. Obtaining all three types of data, even for a single contaminant type, typically requires multiple analyses. For example, to obtain this information with regards to silica would require particle counting (size and concentration data), plus spectrochemical analysis for silicon. When one considers the multitude of contaminant types, it is clear that this is expensive, time-consuming and impractical. Further, there may be more than one contaminant involved, complicating the process of diagnosing and ultimately solving the problem. This is why diagnostic decisions tend to be based on inferences from readily obtainable data, such as engine history, oil analysis, etc. Confirming analyses are only done after there is strong reason to suspect a certain type(s) of contaminant and a more definitive diagnosis is required.
Another issue with conventional approaches is that the particle size and concentration data is divorced from the particle nature data. With the possible exception of laboratory electron microscopic techniques, existing analytical devices provide size and concentration data or nature and concentration data, but do not relate the two. In other words, individual particles are sized and counted, but the supporting chemical analyses tend to be on the bulk or population of particles.
Improved analysis of particles in fluids is needed, including improvements in devices used to analyze the fluids and improvements in the methodology used to analyze data generated by analytical devices.